MetaMask Swap aggregates multiple non-custodial exchanges & traders exchange funds directly through their MetaMask wallet.

MetaMask is a non-custodial wallet and DEX aggregator built on Ethereum and other adjacent blockchains. MetaMask was founded by Aaron Davis and launched in 2015.

MetaMask team & investors own and govern the protocol. They currently take all of the total trading fees paid by traders (revenue).

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MetaMask

# MetaMask Active users (weekly) Methodology

## 1. Overview

Weekly Active Users (WAU) for MetaMask measures the number of unique users who executed at least one trade through MetaMask's DEX aggregation service within a 7-day rolling window. This metric answers the business question "How many distinct users are actively trading through MetaMask each week?" and uses probabilistic counting methods (HyperLogLog) to efficiently calculate unique user counts across time windows while maintaining accuracy at scale.

## 2. Data sources

- **MetaMask Router swap events**: Parsed `swap` function calls from MetaMask Router contracts deployed on Ethereum, BSC, and Polygon blockchains
- **Blockchain transaction records**: Transaction data from Ethereum, BSC, and Polygon networks containing originating user addresses and transaction metadata
- **Chain availability data**: Configuration data that tracks which blockchain networks are operational and properly monitored
- **Token metadata**: Information about token contracts including wrapped native token addresses and denylisted token addresses

## 3. Step-by-step calculation

### Component A: Daily active user identification
- **Purpose**: Extract unique users who executed swaps through MetaMask on each day
- **Logic**: Process MetaMask Router contract `swap` events across Ethereum, BSC, and Polygon networks. Join swap events with blockchain transaction records to identify the originating `user_address` for each trade. Filter for successful swaps where `status` equals true and exclude transactions involving denylisted token addresses. Handle native token trades by converting null address references to the appropriate wrapped native token address for each chain. Group results by day using `timestamp_trunc` to day granularity, extracting unique user addresses per day per chain.
- **Key fields**: `user_address` (originating trader), `block_timestamp` (trade execution time), `chain` (blockchain network), `transaction_hash` (unique trade identifier)

### Component B: HyperLogLog sketch creation  
- **Purpose**: Convert daily user lists into probabilistic data structures for efficient weekly aggregation
- **Logic**: Transform daily active user lists into HyperLogLog sketches with 15-bit precision using BigQuery's `hll_count.init` function. This probabilistic counting approach enables efficient storage and computation of unique user counts across large datasets while maintaining statistical accuracy. Group sketches by day, chain, user type, and product dimensions to preserve breakdown granularity.
- **Key fields**: `hll` (HyperLogLog sketch), `timestamp` (daily granularity), `chain_id` (blockchain identifier), `user_type` (classification as 'Trader')

### Component C: Rolling window construction
- **Purpose**: Create 7-day rolling windows for each calendar day to capture weekly active user periods
- **Logic**: Generate a complete datespine covering all days from the earliest available data through yesterday using `generate_timestamp_array`. For each day in the datespine, create a 7-day lookback window consisting of the current day plus the 6 preceding days. This approach ensures every day has a complete weekly context and handles missing data gracefully by using the datespine to avoid skipped timestamps.
- **Key fields**: `timestamp` (target date), `joinday` (individual days within the 7-day window)

### Component D: Weekly aggregation by dimensions
- **Purpose**: Calculate unique weekly active users across different business dimensions
- **Logic**: For each 7-day window, join the daily HyperLogLog sketches and merge them using BigQuery's `hll_count.merge_partial` function to count unique users across the weekly period. Extract the final count using `hll_count.extract`. Create separate breakdowns for project-level (all MetaMask activity combined), chain-level (individual blockchain networks), user type-level (trader classification), and product-level dimensions. Each breakdown nullifies irrelevant dimension fields while preserving the specific dimension being analyzed.
- **Key fields**: `user_wau` (weekly active user count), `dimensions` (breakdown category), dimension-specific fields like `chain_id`, `user_type`, `product_id`

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project, chain, user_type, product) into a single result set with consistent schema structure
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Weekly periods defined as 7-day rolling windows, with each day representing the WAU for that day and the 6 preceding days
- **Final calculation**: The `user_wau` field contains the final weekly active user count extracted from merged HyperLogLog sketches, representing the estimated number of unique users who traded through MetaMask within each 7-day period

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 7-day window.

MetaMask

Active users (weekly)

Active users (weekly)

Competitive landscape

Chain breakdown